Topics / #continual-learning

Continual-learning projects on GitHub

Continual and meta learning methods that adapt models without forgetting.

As of , the most-starred Continual-learning projects on GitHub are reef (7.6K stars), MetaClaw (3.5K stars) and mini-AGI (1.3K stars). GitHub.Locker tracks 5 Continual-learning repositories with 13.4K stars combined. The fastest-growing on October 18 was ASPIRE, with +136 stars in a day.

5Projects
13.4KStars in total
+167Stars in a day
Browse in the catalog →github.com/topics/continual-learning ↗

Hot right now

Most stars gained in a day · Oct 18

+136

ASPIRE

NVlabs/ASPIRE · ★ 546

ASPIRE (Agentic /Skills Discovery for Robotics) is a research framework where robots improve by refining a library of coding-agent-driven skills instead of training neural network weights. It reaches state-of-the-art results on LIBERO-Pro, Robosuite, and BEHAVIOR-1K benchmarks and can even solve a Rubik's Cube.

+27

reef

Human-Agent-Society/reef · ★ 7.6K

Reef is open-source infrastructure for continually self-improving agents. It wires together agent inference, feedback collection, learning, and versioned delivery so an agent keeps getting better from how you interact with it.

+3

mini-AGI

volotat/mini-AGI · ★ 1.3K

mini-AGI is a continual learning, byte-level language model that assembles its own architecture, trains from scratch on a single 8GB VRAM GPU, and keeps learning from everything it reads, without a separate fine-tuning phase.

+1

MetaClaw

aiming-lab/MetaClaw · ★ 3.5K

MetaClaw sits between your personal AI agent and its LLM, turning every conversation into a learning signal: it injects relevant skills on each turn and can optionally run RL fine-tuning so the agent keeps improving from real use, with no GPU needed for the basic mode.

The all-time top

5 projects · by stars

  1. #1 · Human-Agent-Society/reef

    reef

    Reef is open-source infrastructure for continually self-improving agents. It wires together agent inference, feedback collection, learning, and versioned delivery so an agent keeps getting better from how you interact with it.

    7.6K
  2. #2 · aiming-lab/MetaClaw

    MetaClaw

    MetaClaw sits between your personal AI agent and its LLM, turning every conversation into a learning signal: it injects relevant skills on each turn and can optionally run RL fine-tuning so the agent keeps improving from real use, with no GPU needed for the basic mode.

    3.5K
  3. #3 · volotat/mini-AGI

    mini-AGI

    mini-AGI is a continual learning, byte-level language model that assembles its own architecture, trains from scratch on a single 8GB VRAM GPU, and keeps learning from everything it reads, without a separate fine-tuning phase.

    1.3K
  1. 4

    ASPIRE

    546NVlabs/ASPIRE · Python

    ASPIRE (Agentic /Skills Discovery for Robotics) is a research framework where robots improve by refining a library of coding-agent-driven skills instead of training neural network weights. It reaches state-of-the-art results on LIBERO-Pro, Robosuite, and BEHAVIOR-1K benchmarks and can even solve a Rubik's Cube.

  2. 5

    cadence

    450muellerberndt/cadence · Python

    Cadence is an open research library toward general intelligence: a NumPy collection of 'brains' that compute by settling into an equilibrium and learn by detuning it, aiming to acquire skills from live experience the way animals and humans do.

Often tagged together